… based on nonlinear features extraction, using a hierarchical self-organizing map HSOM. A …
of the field of self-organizing systems for data analysis, relate to the self-organizing maps core …

… In this work, we want to use AEs as feature extractors with unsupervised learning. In such
cases, the feature map representation of a Convolutional AE (CAE) is most of the time of a …

… The proposed architecture takes advantage of consolidated techniques, as self-organizing
map clustering (SOM) [18] and object-based image analysis (OBIA) [19], and exploits the char…

… approach using Self Organizing Maps (SOM), which is an unsupervised classification
method, was proposed. There are few samples for airborne Lidar feature extraction using SOM …

… for image representation is a major goal for many computer vision and pattern recognition …
the performance of proposed DCSOM feature extraction method by changing the most …

… to the implementation of DeepCluster [20], we separate learning feature extractor and feature
… In addition, we also intend to apply the proposed methods on other computer vision tasks, …

… One of such challenges is poor underwater vision resulting … echo images on a frontal vision
since acoustic signals can pass … An optical camera was employed in [2] for visual feedback …

… In this work a novel attempt taking advantage of capabilities of a self-organizing map and …
main purpose of feature extraction is representing the original data set by proper features to …

… increasing attention in computer vision society during the past … to represent and extract
suitable facial features from raw … In this paper, we propose a novel feature extraction method …

… Self-Organizing Map (SOM). Based on the SOM, SO-Net performs hierarchical feature
extraction … represents the input point cloud by a single feature vector. The receptive field of the …